Estimation of Interest Levels From Behavior Features via Tensor Completion Including Adaptive Similar User Selection

A method for estimating interest levels from behavior features via tensor completion including adaptive similar user selection is presented in this paper. The proposed method focuses on a tensor that is suitable for data containing multiple contexts and constructs a third-order tensor in which three modes are “products”, “users” and “user behaviors and interest levels” for these products. By complementing this tensor, unknown interest level estimation of a product for a target user becomes feasible. For further improving the estimation performance, the proposed method adaptively selects similar users for the target user by focusing on converged estimation errors between estimated interest levels and known interest levels in the tensor completion. Furthermore, the proposed method can adaptively estimate the unknown interest from the similar users. This is the main contribution of this paper. Therefore, the influence of users having different interests is reduced, and accurate interest level estimation can be realized. In order to verify the effectiveness of the proposed method, we show experimental results obtained by estimating interest levels of users holding books.

[1]  Miki Haseyama,et al.  Interest Level Estimation of Items via Matrix Completion Based on Adaptive User Matrix Construction , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[2]  Bamdev Mishra,et al.  A Dual Framework for Low-rank Tensor Completion , 2017, NeurIPS.

[3]  Hiroyuki Kidokoro,et al.  Effectiveness of Cooperative Customer Navigation from Robots around a Retail Shop , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[4]  Bharati Wukkadada,et al.  Just Walk-Out Technology and its Challenges: A Case of Amazon Go , 2018, 2018 International Conference on Inventive Research in Computing Applications (ICIRCA).

[5]  Léon J. M. Rothkrantz,et al.  Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information , 2013, Pattern Recognit. Lett..

[6]  Yimin Wei,et al.  Randomized algorithms for the approximations of Tucker and the tensor train decompositions , 2018, Advances in Computational Mathematics.

[7]  Miki Haseyama,et al.  Few-Shot Personalized Saliency Prediction Based on Adaptive Image Selection Considering Object and Visual Attention† , 2020, Sensors.

[8]  Miki Haseyama,et al.  Accurate Estimation of Personalized Video Preference Using Multiple Users' Viewing Behavior , 2018, IEICE Trans. Inf. Syst..

[9]  David C. Yen,et al.  Data mining techniques for customer relationship management , 2002 .

[10]  Stephen H. Fairclough,et al.  Classification Accuracy from the Perspective of the User: Real-Time Interaction with Physiological Computing , 2015, CHI.

[11]  Jinglei Lv,et al.  What Makes a Good Movie Trailer?: Interpretation from Simultaneous EEG and Eyetracker Recording , 2016, ACM Multimedia.

[12]  Miki Haseyama,et al.  A Novel Framework for Estimating Viewer Interest by Unsupervised Multimodal Anomaly Detection , 2018, IEEE Access.

[13]  Hong Yan,et al.  Tagrec-CMTF: Coupled Matrix and Tensor Factorization for Tag Recommendation , 2018, IEEE Access.

[14]  Miki Haseyama,et al.  Multimodal Interest Level Estimation via Variational Bayesian Mixture of Robust CCA , 2016, ACM Multimedia.

[15]  Yin Zhang,et al.  Fairness-Aware Recommendation of Information Curators , 2018, ArXiv.

[16]  Miki Haseyama,et al.  Interest Level Estimation Based on Tensor Completion via Feature Integration for Partially Paired User’s Behavior and Videos , 2019, IEEE Access.

[17]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[18]  James Caverlee,et al.  Tensor Completion Algorithms in Big Data Analytics , 2017, ACM Trans. Knowl. Discov. Data.

[19]  Miki Haseyama,et al.  Interest level estimation based on matrix completion via rank minimization , 2017, 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE).

[20]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[21]  Jing Xu,et al.  Deep Attentive Factorization Machine for App Recommendation Service , 2019, 2019 IEEE International Conference on Web Services (ICWS).

[22]  James T. Kwok,et al.  Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations , 2019, ICML.

[23]  Ale Smidts,et al.  Brain Responses to Movie Trailers Predict Individual Preferences for Movies and Their Population-Wide Commercial Success , 2015 .

[24]  Jiuyang Tang,et al.  Efficient and Accurate Traffic Flow Prediction via Incremental Tensor Completion , 2018, IEEE Access.

[25]  Junlin Zhang,et al.  FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine , 2019, ICDM.

[26]  Yunchao Wei,et al.  Low-Rank Tensor Completion With a New Tensor Nuclear Norm Induced by Invertible Linear Transforms , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Jieping Ye,et al.  Tensor Completion for Estimating Missing Values in Visual Data , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Yanlei Gu,et al.  Customer behavior classification using surveillance camera for marketing , 2017, Multimedia Tools and Applications.

[29]  Yanlei Gu,et al.  Customer Behavior Recognition in Retail Store from Surveillance Camera , 2015, 2015 IEEE International Symposium on Multimedia (ISM).

[30]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[31]  Qing Liu,et al.  A Truncated Nuclear Norm Regularization Method Based on Weighted Residual Error for Matrix Completion , 2016, IEEE Transactions on Image Processing.

[32]  Xing Xie,et al.  xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems , 2018, KDD.

[33]  Emmanuel J. Candès,et al.  Exact Matrix Completion via Convex Optimization , 2008, Found. Comput. Math..

[34]  Shenghua Gao,et al.  Personalized Saliency and Its Prediction , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.